Process csv file

In [ ]:

import csv
import pprint
from typing import List, Iterable, Optional

import csv import pprint from typing import List, Iterable, Optional

In [ ]:

from presidio_analyzer import BatchAnalyzerEngine, DictAnalyzerResult
from presidio_anonymizer import BatchAnonymizerEngine

from presidio_analyzer import BatchAnalyzerEngine, DictAnalyzerResult from presidio_anonymizer import BatchAnonymizerEngine

In [ ]:

"""
Example implementing a CSV analyzer

This example shows how to use the Presidio Analyzer and Anonymizer
to detect and anonymize PII in a CSV file.
It uses the BatchAnalyzerEngine to analyze the CSV file, and 
BatchAnonymizerEngine to anonymize the requested columns.

Content of csv file:
id,name,city,comments
1,John,New York,called him yesterday to confirm he requested to call back in 2 days
2,Jill,Los Angeles,accepted the offer license number AC432223
3,Jack,Chicago,need to call him at phone number 212-555-5555

"""

""" Example implementing a CSV analyzer This example shows how to use the Presidio Analyzer and Anonymizer to detect and anonymize PII in a CSV file. It uses the BatchAnalyzerEngine to analyze the CSV file, and BatchAnonymizerEngine to anonymize the requested columns. Content of csv file: id,name,city,comments 1,John,New York,called him yesterday to confirm he requested to call back in 2 days 2,Jill,Los Angeles,accepted the offer license number AC432223 3,Jack,Chicago,need to call him at phone number 212-555-5555 """

In [ ]:

class CSVAnalyzer(BatchAnalyzerEngine):

    def analyze_csv(
        self,
        csv_full_path: str,
        language: str,
        keys_to_skip: Optional[List[str]] = None,
        **kwargs,
    ) -> Iterable[DictAnalyzerResult]:

        with open(csv_full_path, 'r') as csv_file:
            csv_list = list(csv.reader(csv_file))
            csv_dict = {header: list(map(str, values)) for header, *values in zip(*csv_list)}
            analyzer_results = self.analyze_dict(csv_dict, language, keys_to_skip)
            return list(analyzer_results)

class CSVAnalyzer(BatchAnalyzerEngine): def analyze_csv( self, csv_full_path: str, language: str, keys_to_skip: Optional[List[str]] = None, **kwargs, ) -> Iterable[DictAnalyzerResult]: with open(csv_full_path, 'r') as csv_file: csv_list = list(csv.reader(csv_file)) csv_dict = {header: list(map(str, values)) for header, *values in zip(*csv_list)} analyzer_results = self.analyze_dict(csv_dict, language, keys_to_skip) return list(analyzer_results)

In [ ]:

if __name__ == "__main__":

    analyzer = CSVAnalyzer()
    analyzer_results = analyzer.analyze_csv('./csv_sample_data/sample_data.csv',
                                            language="en")
    pprint.pprint(analyzer_results)

    anonymizer = BatchAnonymizerEngine()
    anonymized_results = anonymizer.anonymize_dict(analyzer_results)
    pprint.pprint(anonymized_results)

if __name__ == "__main__": analyzer = CSVAnalyzer() analyzer_results = analyzer.analyze_csv('./csv_sample_data/sample_data.csv', language="en") pprint.pprint(analyzer_results) anonymizer = BatchAnonymizerEngine() anonymized_results = anonymizer.anonymize_dict(analyzer_results) pprint.pprint(anonymized_results)